Julia also supports transparent Cluster API and CUDA integration. i.e. the hot-mess you hope to never have to maintain professionally.
In my humble opinion, the utility of this high-level language makes it fundamentally different. Thus, it is slowly becoming more mainstream as the ecosystem stabilizes.
Remember to have fun =)
You can have "very high level functional programming" and 8x the speed of Julia in a mathematics-oriented language!
The danger of course is when Wolfram takes those assumptions into a room of mathematicians. It is controversial for all the wrong reasons. =)
It is a common question by the way =)
https://stackoverflow.com/questions/60121757/julia-vs-mathem...
The reason Mathematica is so much faster here is it’s using a different algorithm. When you compare using the same algorithm, Julia is 10-100x faster than Mathematica. https://julialang.org/benchmarks/
Yet a polyglot project is bad design, and tends to become an abomination in time. This is one reason many Go programmers rewrote libraries rather than saturate their code with cgo/C and SWIG/C++ wrappers. Similarly, people are writing scalable versions of libraries in Julia to improve transparent parallel performance for problems too big for a single machine to feasibly handle.
Things are still undergoing change, but of course the commercial nvidia binary blobs will haunt everyone for awhile. =)
x*(y.^2)
To the Python equivalent:
np.matmul(x, map(lambda x : x^2, y))
Note also that the first will be much faster because it can fuse the matrix multiplication with the exponentiation. That’s because Julia is a single language, and the whole thing is compiled at the same time. Python can’t fix the above code because NumPy is written in C, not Python.
Julia is just Python but fast.